Freeway networks, despite being built to handle the transportation needs of large
traffic volumes, have suffered in recent years from an increase in demand that is
rarely resolvable through infrastructure improvements. Therefore, the implementation
of particular control methods constitutes, in many instances, the only viable solution
for enhancing the performance of freeway traffic systems. The topic is fraught with
ambiguity, and there is no tool for understanding the entire system mathematically;
hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this
study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed
as tools to analyze freeway traffic data with the objective of traffic flow modeling
at a macroscopic level in order to address congestion-related issues as the primary
goal of the traffic control strategies. In addition to presenting a framework of fuzzy
system-based controllers in freeway traffic, the results of this study demonstrated
that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level
prediction, traffic flow simulation, and scenario analysis, thereby enhancing the
performance of the traffic control strategies involving the implementation of ramp
management policies, controlling vehicle movement within the freeway by mainstream
control, and routing control.
